RESULTS: We describe the effect of log10 transformation of the reference values, regular PLS, robust PLS, the newly introduced bin PLS, and their combinations to select more evenly distributed reference values for the quantification of five pulp characteristics (kappa number, R18, R10, cuen viscosity, and brightness; 200 samples) by near-infrared spectroscopy. The quality of the models was assessed by root mean squared error of prediction, calibration range, and coverage of sample types. The best models yielded uncertainty levels equivalent to that of the reference measurement. The optimal approach depended on the investigated reference value.
CONCLUSIONS: Robust PLS commonly gives the model with the lowest error, but this usually comes at the cost of a notably reduced calibration range. The other approaches rarely impacted the calibration range. None of them stood out as superior; their performance depended on the calibrated parameter. It is therefore worthwhile to investigate various calibration options to obtain a model that matches the requirements of the application without compromising calibration range and sample coverage.
结果:我们描述了参考值的log10转换的效果,常规PLS,强大的PLS,新推出的binPLS,及其组合,以选择更均匀分布的参考值来量化五种纸浆特征(卡伯值,R18,R10,Cuen粘度,和亮度;200个样品)通过近红外光谱法。通过预测的均方根误差评估模型的质量,校准范围,和样本类型的覆盖范围。最好的模型产生的不确定度水平与参考测量值相同。最佳方法取决于所研究的参考值。
结论:鲁棒PLS通常给出误差最小的模型,但这通常是以明显缩小的校准范围为代价的。其他方法很少影响校准范围。它们都没有脱颖而出。它们的性能取决于校准参数。因此,值得研究各种校准选项,以获得符合应用要求的模型,而不影响校准范围和样品覆盖率。